Spatial Data on the Web Use Cases & Requirements. Use Cases. Use cases that describe current problems or future opportunities for spatial data on the Web have been gathered as a first activity of the Working Group. They were mainly contributed by members of Working Group, but there were also contributions from other interested parties. In this chapter these use cases are listed and identified. Each use case is related to one or more Working Group deliverables and to one or more requirements for future deliverables. Meteorological data rescue. In order to find out the requirements for the deliverables of the Working Group, use cases were collected. For the purpose of the Working Group, a use case is a story that describes challenges with respect to.Readbag users suggest that Microsoft Word - 10icud - Abstract collection. The file contains 68 page(s) and is free to view, download or print. See also: HYDRUS (2D/3D) selected references; Unsatchem References; HP1 References; Hydrus-1D Selected References. If your publication is not on the list below, please email us the reference and/or send a. Since the last five years, Castleton based in India have made life easier for clients in UK, Germany and Australia by assisting them with bookkeeping, accounting & data management. Our accounting outsourcing service is. Chris Little, based on scenarios used for the WMO infrastructure requirements. WMO has the same status as ISO, and its standards and regulatory materials applies to all its 1. WMO has embarked on a long- term (think a decade or so) program to update the global meteorological operational infrastructure. This is known as the WIS (WMO Information System). The global infrastructure also has aviation, oceanographic, seismic and other users. The WIS includes a global, federated, synchronized, geospatial catalog, envisaged to encompass all hydro- meteorological data and services. Institute Center for Water and Environment (iWATER), Masdar Institute of Science and Technology, Masdar City, P.O. Box 54224, Abu Dhabi, UAE The United Arab Emirates (UAE) has an arid climate with low average annual. Readbag users suggest that Abstract-Volume-2011.pdf is worth reading. The file contains 360 page(s) and is free to view, download or print. GC Improving Resolution and Clarity with Neural Networks, Christopher P. Characterize Millimeter-Scale Unconventional Rock Using Micron-Scale Sample Imaging and Machine Learning, Radompon Sungkorn. Currently several nodes are operational, cataloging mainly routinely exchanged observations and forecasts. Envisage an environmental scientist in Cambodia, researching the impact of deforestation in Vietnam as part of investigating the regional impacts of climate change. She submits her search keywords, in Cambodian, and receives responses indicating there is some data from the 1. Biblioth. She receives an abstract of some form that enables her to decide that the data are worth accessing, and initiates a request for a digital copy to be sent. She receives the pamphlet as a scanned image of each page, and she decides that the quantitative information in the paper is useful, so she arranges transcription of the tabular numerical data and their summary values into a digital form and publishes the dataset, with a persistent identifier, and links it to a detailed coverage extent, the original paper source, the scanned pages and her paper when it is published. She also incorporates scanned charts and graphs from the original pamphlet into her paper. Her organization creates a catalog record for her research paper dataset and publishes it in the WIS global catalog, which makes it also visible to the GEO System of Systems broker portal. Spatial Data on the Web Best Practices, 2. Time Ontology in OWL, 2. Semantic Sensor Network Vocabulary, 2. Coverage in Linked Data. Spatial metadata, 5. Coverage temporal extent, 5. CRS definition, 5. Date, time and duration, 5. Different time models, 5. Discoverability, 5. Georeferenced spatial data, 5. Linkability, 5. 2. Multilingual support, 5. Nominal temporal references, 5. Observed property in coverage, 5. Provenance, 5. 3. Quality metadata, 5. Reference data chunks, 5. Reference external vocabularies, 5. Sensing procedure, 5. Sensor metadata, 5. Space- time multi- scale, 5. Spatial vagueness, 5. Temporal reference system, 5. Uncertainty in observations, 5. Crawlability. 4. 2 Habitat zone verification for designation of Marine Conservation Zones. Jeremy Tandy. MCZs protect a range of nationally important marine wildlife, habitats, geology and geomorphology and can be designated anywhere in English and Welsh inshore and UK offshore waters. The designation of a MCZ is dependent on a detailed analysis of the marine environment which results in the definition of geometric areas where a given habitat type is deemed to occur and is published as a habitat map. Being a policy statement, it is important to be able to express the provenance of information that was used to compile the habitat map. Moreover, because the marine environment is always changing, it is important to express the time at which this information was collected. The information includes: acoustic surveyvideo (from a video camera towed behind a survey boat)biota observations (based on what is observed in the video and from physical collection)particle size (sand/mud)water column dataseabed character map: discrete seabed features and backscatter information (from sonar) to determine bottom type. These information types are varied in type and size. In particular, the acoustic survey (e. A way is needed to refer to just a small part of these coverage data sets that are relevant to a particular habitat zone analysis. Spatial Data on the Web Best Practices, 2. Time Ontology in OWL, 2. Semantic Sensor Network Vocabulary, 2. Coverage in Linked Data. Georeferenced spatial data, 5. Multiple types of coverage, 5. Provenance, 5. 3. Reference data chunks, 5. Sensor metadata, 5. CRS definition, 5. Reference external vocabularies, 5. Mobile sensors, 5. Linkability, 5. 2. Nominal observations, 5. Humans as sensors, 5. Support for 3. D, 5. Ex- situ sampling, 5. Sensing procedure, 5. Spatial relationships. Real- time wildfire monitoring. Manolis Koubarakis. The wildfire monitoring service is based on the use of satellite images originating from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor on top of the Meteosat Second Generation satellites MSG- 1 and MSG- 2. Since 2. 00. 7, NOA operates an MSG/SEVIRI acquisition station, and has been systematically archiving raw satellite images on a 5 and 1. MSG- 1 and MSG- 2. The service active in NOA before TELEIOS can be summarized as follows: The ground- based receiving antenna collects all spectral bands from MSG- 1 and MSG- 2 every 5 and 1. The raw datasets are decoded and temporarily stored in the METEOSAT Ground Station as wavelet compressed images. A Python program manages the data stream in real- time by offering the following functionality. Extract and store the raw file metadata in an SQLite database. This metadata describes the type of sensor, the acquisition time, the spectral bands captured, and other related parameters. Such a step is required as one image comprises multiple raw files, which might arrive out- of- order. Filter the raw data files, disregarding non- applicable data for the fire monitoring scenario, and dispatch them to a dedicated disk array for permanent storage. Trigger the processing chain by transferring the appropriate spectral bands via FTP to a dedicated machine and initiating the following steps: (i) cropping the image to keep only the area of interest, (ii) georeferencing to the geodetic reference system used in Greece (HGRS 8. The burnt scar mapping service is dedicated to the accurate mapping of burnt areas in Greece after the end of the summer fire season, using Landsat 5 TM satellite images. The processing chain of this service is divided into three stages, each one containing a series of modules. The pre- processing stage is dedicated to (i) identification of appropriate data, downloading and archiving, (ii) georeferencing of the received satellite images, and (iii) cloud masking process to exclude pixels “contaminated” by clouds from the subsequent processing steps. The core processing stage comprises (i) a classification algorithm which identifies burnt and non- burnt sets of pixels, (ii) a noise removal process that is necessary to eliminate isolated pixels that have been classified wrongfully as burnt, and (ii) converting the raster intermediate product to vector format. Finally, the post- processing stage consists of (i) a visual refinement step to ensure product thematic accuracy and consistency, (ii) attribute enrichment of the product by overlaying the polygons with geoinformation layers and finally (iii) generation of thematic maps. It would be interesting for NOA to see where the standards to be developed in this working group could be used. Spatial Data on the Web Best Practices, 2. Time Ontology in OWL, 2. Semantic Sensor Network Vocabulary. CRS definition, 5. Georectification, 5. Linkability, 5. 2. Multiple types of coverage, 5. Provenance, 5. 3. Sensor metadata, 5. SSN- like representation. Harvesting of Local Search Content. Ed Parsons. Local search providers spend much time and effort creating databases of local facilities, businesses and events. Much of this information comes from Web pages published on the public Web, but in an unstructured form. Previous attempts at harvesting this information automatically have met with only limited success. Current alternative approaches involve business owners manually adding structured data to dedicated portals. This approach, although clearly an improvement, does not really scale and there are clearly issues in terms of data sharing and freshness. The information of interest includes: the facility's address; the type of business/activity; opening hours; date, time and duration of events; telephone, e- mail and Web site details. Complexities to this include multiple address standards, the differences between qualitative representations of place, and precise spatial co- ordinates, definitions of activities etc. Ultimately these Web pages should become the canonical source of local data used by all Web users and services. Spatial Data on the Web Best Practices, 2. Time Ontology in OWL,5. Date, time and duration, 5. Crawlability, 5. 1. Discoverability. 4. Locating a thing. Ed Parsons. While the determination of sensor in space to a high level of precision is a largely solved problem we are less able to express the location in terms meaningful to humans. The fact that the Bluetooth- LE tracker attached to my bag is at 5. At others times the location descriptions . A new scale of geospatial analysis may be required using a reference frame based on the locations of individuals rather than a global spherical co- ordinate, allowing a location of your keys and their attached bluetooth tag to be described as . The point of this use case is that it would be good to remove barriers that stand in the way of more spatial data becoming available on the Web. A data publisher could have the following questions: How should I publish vector data? What is the best encoding to use? How should I publish raster data? How do I make the CRS known? How do I make the spatial resolution/level of detail/accuracy known?
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